{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# project-based method with gc\n",
    "all_seeds = [0, 10, 123, 231, 321]\n",
    "\n",
    "all_results = []\n",
    "for seed in all_seeds:\n",
    "    fn = 'exp3_results/exp3_unlearn_gc_20_18188.000000_seed%d.pkl'%seed\n",
    "    with open(fn, 'rb') as f:\n",
    "        results = pickle.load(f)\n",
    "        all_results.append(results)\n",
    "        \n",
    "all_results = np.array(all_results)\n",
    "projector_gc_mean = np.mean(all_results, axis=0)\n",
    "projector_gc_stds = np.std(all_results, axis=0)\n",
    "\n",
    "# project-based method\n",
    "all_seeds = [0, 10, 123, 231, 321]\n",
    "\n",
    "all_results = []\n",
    "for seed in all_seeds:\n",
    "    fn = 'exp3_results/exp3_unlearn_20_18188.000000_seed%d.pkl'%seed\n",
    "    with open(fn, 'rb') as f:\n",
    "        results = pickle.load(f)\n",
    "        all_results.append(results)\n",
    "        \n",
    "all_results = np.array(all_results)\n",
    "projector_mean = np.mean(all_results, axis=0)\n",
    "projector_stds = np.std(all_results, axis=0)\n",
    "\n",
    "\n",
    "# GCN method\n",
    "all_seeds = [0, 10, 123, 231, 321]\n",
    "\n",
    "all_results = []\n",
    "for seed in all_seeds:\n",
    "    fn = 'exp3_results/exp3_retrain_gcn_20_18188.000000_seed%d.pkl'%seed\n",
    "    with open(fn, 'rb') as f:\n",
    "        results = pickle.load(f)\n",
    "        all_results.append(results)\n",
    "        \n",
    "all_results = np.array(all_results)\n",
    "gcn_mean = np.mean(all_results, axis=0)\n",
    "gcn_stds = np.std(all_results, axis=0)\n",
    "\n",
    "\n",
    "# GraphSAGE method\n",
    "all_seeds = [0, 10, 123, 231, 321]\n",
    "\n",
    "all_results = []\n",
    "for seed in all_seeds:\n",
    "    fn = 'exp3_results/exp3_retrain_graphsage_20_18188.000000_seed%d.pkl'%seed\n",
    "    with open(fn, 'rb') as f:\n",
    "        results = pickle.load(f)\n",
    "        all_results.append(results)\n",
    "        \n",
    "all_results = np.array(all_results)\n",
    "graphsage_mean = np.mean(all_results, axis=0)\n",
    "graphsage_stds = np.std(all_results, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"font.family\"] = \"serif\" \n",
    "plt.rcParams[\"figure.figsize\"] = (6,2.5)\n",
    "\n",
    "SMALL_SIZE = 11\n",
    "MEDIUM_SIZE = 11\n",
    "BIGGER_SIZE = 12\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AeF5KeeiuHotCsTegzKMKhULRxP0k+FoKa83Yw0jiCrC3IoTIFUJca59eDTy+K8ejUOxNKE2bYq/Fjnr7C1ZS0KVYGoO7du2oFLsSIcTpWMEgUSwToYaVoPb9XTqw3Qjb93MeVvLdRVi5B9v021UoFN2DEtoUCoVCoVAo9gB2qnnUDm+fYad0eNf2h2jZ5lMhxOaErU4IcWOLNiVCiKgQYuzOGrtCoVAoFArFrmRn+7Q9gRVJNQUrUu3pJG0WSynz4xtW9Nir8Uo7lcTvscL9FQqFQqFQKPYKdpp5VAiRjZV9P0VKGbQTOvqxUhxsbOWaPsADUsozEsruxUqN8CqWL8XM7d07JydH9uvXr9Njr6+vx+fzdfp6hZrD7kLNY/eg5rHrdHUO586dWy6lbLkMXSPz5s07zjCM2+wf7ypoTrE3EAO+jkajl40ePTqcrEGry5/sAPoCfmmtlYmUMiyEqMLKRJ9UaAOmYOWpAkAIcRgQkVLO2l7icyHE5cDlAHl5edx///2dHrjf7yclpb1LZSqSoeawe1Dz2D2oeew6XZ3DcePGrWmtbt68ece5XK7H+vXrF/Z4PFWapinna8UvHtM0xZo1a35VXV19BdayfduwM4W2DmFnaD9USjnVPvdhJUA9o80LbaSUT2ObX8eMGSPHjh3b6bHMnDmTrlyvUHPYXah57B7UPHadHTmHhmHc1q9fv7DP52vYITdQKHZDNE2TBQUF/rq6usnsBkLbGiBFCOFOMI9mYmUQT8YZwOsJ54djqQ4fsbVs2cDvhBAlUsp7k1yvUCgUij0QKWW+x+Op2tXjUCh2Nk6nMyKlzGqtfqcJbVLKCiHER8BxWMsJHQN8LaXcaK8/+bm01qSMcwEwMeH6/wD/iZ8LIY4HHmyPT1uXMGNgRqC2NQtuW9gmXCFAc4Bu2Ht7UygUCkUyNGUSVeyN2EqpVn04d7Z59EosTdkJQCG2zxnWOo5TsNa4QwixP7BcSlnbsgMhRB7wB6y1Ea8VQmRIKWfssBFLCaYJ4UD39isEaHqTEKcZ9j5+rnfv/RQKhUKhUOzR7FShTUpZirUYe8vyES3O52Fl3E7WRxnWgsm/2QFDbM5718GClznMNGGWDghL2BJa0zH2eeNxvJ6mdoYLUvIgtSek5tubfezrkVzrJkRzIU53gO4E3QWaCqRSKBSKPZFvv/3Wc9111/VZsGCBz+12m+vWrVuYlpa2zYoS999/f86NN97Yt7i4uOGMM86o+POf/1y2K8bbHlavXu0oKira5957711z/fXXlyfW3X333bnTp0/PXbp0qWefffapv/766zdfcMEF1QB//etfc6dNm5ZbXl7uGDVqlP/TTz9dGb/u888/99555509q6urDU3TCIfDYujQoQ1nnHFG1a9//etabS/9HtxtAxF2C2IRiAbRAbq6SEvV6lYqBPhymwtzKS0EO1dq80s03RbgnAnCnNMyv3YVKcGMtthiVrnQLIFR6NYYRMKx0gwqFArFdjnkkEMaZs+evbSwsHDkli1bHH/7299ybrnlli2JbUzT5Omnn+4BcP/996876aST6nbNaNvHtGnTsoQQvPrqq9kthbapU6du3WeffYInn3zy4Ndee21VcXFxYyqLm2++eevw4cOD06ZNy37zzTdLE/rL/N3vftfnlVdeWXncccf5ASorK7Xzzz+/38SJEwdt3bp1fk5OTmynPeBuhBLa2mRnuFRIqN9ibZsXJm/i9FkCXFohpBVAaoG1TyuEtJ7gtMPuNa0VYc7W5JmxbYWxbQS0LkincZNvMqFOxiBYa5cl2fbSX00KhWLvZfz48VVPPvlkj5tvvnmLYTR9Hb/66qvpw4cPDyxfvtyzC4fXbmbMmJF52WWXbX7qqafyV6xY4SgqKop0tq8NGzYYv/nNb/pNnTp1Q1xgA8jKyjJfeeWV0p49e6Z3z6j3TJTQ1hYnP0L02L/wxczPKO6Xb68eLRFINIG1YSIQCEw0QBMSISSatNqARIvWo/u3oPnLoG4T+DdD3WbruL6c7QqH4XqoWGFtyXCl20JcwpaaINQ57P9704RoEEK1lgAVrLGPayBU1+LcbhPfm1Fwp4ErDdwZ1rE7zbp34t6dbm0u+9hwW8JcLAr+LcnHH0ckmJdb2xrN0pDcVN2KyTpepoRDhUKxm3DTTTdtPvTQQ4dNnz498+KLL26Mlv373//e44477tg4Y8aM7HjZ8uXLnddee22vjRs3Og3DkE6nU/7tb39bO3r06GC8zbp164wrrriiz4oVK9w+ny8mhODqq68uu/DCC6snTpzYb+bMmemHHXZYTUFBQeSHH37wzZkzJ+X+++9fc80111S8+eababfffnuhEEIGg0Ft0qRJFbfffvt2TbJz58515+TkRK+//votTz31VP60adOy77rrrs2dnZOnnnoqOxAIaJMnT65sWZeammq+9NJLK1JTU7tq+9pjUUJbGzzx1Vr+u2gTDXVOMlcHceoClyGsvS5wGgKXruHSwakbjXWN7TRwGQKXnopL74krXcOTLfA4NTwODY8h0GUMI1iOUb8JvX4Lmn8zmn8zwl+G8NuCXTTU9kBDNbC1BrYuSV7vtf/vg7VWJGxnqd9qbR1Bc4A7jQPwwMocW6BLTRD20lrs4/WpO8bkGhf0mmkEEzSDScvbTuTcJvEVR6QEZIIwqVAo9nYOOeSQhoMPPrjukUceyYsLbV999ZW3Z8+e4cLCwmYf1gsWLHCbpsm8efN+1jSNxx9/POv0008vWr58+U8Oh2VNOeWUU4oGDRrU8PPPPy8G+POf/9zj8ccfz7vwwgur33jjjdLTTz+930cffZTx73//e9njjz++4Y9//GOew+GQc+fOdZ999tlFM2bMWDZ+/Hj/mjVrHAcccMDQ1NTUWEtzZ0uee+657LPOOquyqKgoMnr0aP/rr7+e1RWh7fvvv/elpaXFCgsLo8nqJ0yYsFubinc0Smhrg9Lyeuavs7OQVHdz9KhNXMBz6Tm49Byc+nD7XOA0NDxZkKPXUeyqoMhRTh9RTq65hdTgZjT/JkTdJoglXe2iiUDFDhl7uzAjEKjABxBY37FrXamWMBcX6HSXFWWrJQhVugEiXma0KE8o0+x0K4YrYXNbm96yzN5rRnPtn6Y3CWHI5seQcN7iuCVxv0ChNR9zM39BvXm5QqH4RXLddddtPuusswb997//TTnuuOP89913X94f//jHTS3bHXPMMf7DDjusPu6Af/HFF1f95je/6b948WLXqFGjQu+9917qDz/84Js2bVqjA/W1115bHgwGm32ADB06tOGwww4LAPzpT38qAzjttNP6DR8+PDB+/Hg/QN++fSMTJ06sePDBB3u2JbSZpsl///vf9LvvvnsTwJlnnllx00039Z09e7anpKSkU4mRa2pqdK/Xu1f6q7UHJbS1QTC649834ZgkHJO0/dPBAeTbm12iQX6qQa8MjaG+OopdlQwwyilgK1nRMpz1m9D8lilWmAnPoTsTzJdpTWbMxn1q8nOhW6bSUC0Eq23zaq2l5UvcB2uam1m3J1C2RajO2tjQ+T66gtCaC3K6q4XZVWs6byxPLIufJ9S708GbC76chC0XvDmWb2IyLVyi0BiLWKb1Rn/BRAGwpT+h0ugpFLs7EydOrB04cGDwvvvuyxs4cGCorq5OHzNmTHDp0qXOxHa6rst77rkn/8svv0zVNC2ez4v169c7Ro0aFVq4cKFHCMGwYcMaTTNpaWlmS61XQUHBNh/KS5cu9QwaNCiYWFZUVBTauHGjs6qqSnv++eczX3jhhZx43ezZs5cCfPLJJ76hQ4c2xM2VF1xwQdXUqVP7TJs2LaukpKTxg1vXdQmQbK1zKSWJkaAZGRmxhoYGFdnWCkpoa4Prjynm3JI+fDdnHrl9BxOKmoSjpr2PNR6HoibhWGKdSciub15mtYvXRWKdD3SImLCuJsq6GvgOF9DT3ixyvToFaQa9C2Cwt45UlwHuVAyHG5cucBkaLgNchoZT13A5NJyGjtuh4TQ0XIaOEBpCEwh0y7LnTAFHHlF3jGA4SjASIxiFhohJMCYJRkwaopJgVFr7iEk0EoRgLdVbN1Lok7hjdXhifjymvY/58cTq8NrnXtOPN+bHI+u7/gJ2FWlCpMHadgaGu0mIiwtyvgQBz5uLEa6BhmpLQ7c9GgNDWkT8tou2BL5kGsWW2sYk2se4drKlgJnMJK2ikRV7CZqmcdVVV5XdcMMNfa+44gquvvrqpM6/V1xxRe/PPvss/bvvvlvSp0+fKIAQYrSUskO/zuICVEe45pprKq655pptTDbTp0/PXrJkiaekpKQ4XpaSkhKbMWNG1uOPP74hLoylpaXFwNKiteyjurpaT01NbdQsHHjggf7//Oc/mRs2bDBaM5HuzSihrQ36ZHvplemhttRg3+GWlqvRRanxS6mpfePXVbMy60TYX4KNPvRAzJTNhb2YSSgSayEcmvhDUdZVBlhXFWB9VQMbqxuoCrTtm7Y1EGNrIMYCAOI/2OrtrX04bBOty9ARYAtpJjGzo//zBtCnQ1foxEglQIbwk0E96aIeJxF0TBxE0TExRAyfHiPTBelOSYZTku40SXNAqsMkzSFJcUh0YaLJGCIWQcRCiGgQEQtBLISIJuyjIUQsCNEwRIMIuZM19NEg1Ky3tlb4FcAswOG1tKDOlCYfQFcqOFPBlbJtnTPeJsUKTNFdu7cmri3fw5Zlje124+dRKNpgypQpFXfddVfhmjVrXKeddto2SeUBZs2alXLggQfWxQW2YDDY7A2/zz77NEgpiZtLAaqqqrRHHnkkd3sBBcXFxQ2rVq1yJ5atXLnSVVBQEM7MzEzq9B8KhcT//ve/1CVLliyK+9QB/OMf/8i87LLLBvznP/9JiZtbR44cGTIMQ86bN89z4IEHNvsVvHDhQs+wYcMay6ZMmVJx//33F/zrX//KnDp1ajMn6sWLFzv33XffET/++ONPialD9iaU0LYdNE1gaIKcFNeuHkojkZhJdSDM6q0BVlfUs6aynnWVATZUNbCxOsiWuiAdlquS3kcSicWoD+1894IYOtWkUi3tHHWtPU8MaONfVwA5Xp28FJ0017ZaJhlv5LA3moRuXUZxyjBOwo17jw4uXeJ2CLw6uHXwOKS1N8BtgMcQuHVwa/FzcBsSgxh6uAZRX44WqEAEytEC5YhAOQS2WoJje4kErI3O5tsUlvAW3wxP8nPDA84W58iE/H1RKzJYxhJSyMSa1zeW2eW6wxIonSlWOhtXStN54rHT1xR93K5HaplyprUAE816hmi46brEeUnsr+WcxcuVgKjoRjwej3zuuedWJWqcWjJo0KDgvHnzUmpra7W0tDTz+eefz0isP/nkk+v222+/+rvvvjv/1VdfXQPwl7/8JW/z5s3bXTPxlltu2XzQQQcN+/DDD1OOP/54/9q1a4033ngj+6abbmp1/ca33nor7bDDDqtNFNgAJk2aVP3b3/429vzzz2fHhba0tDTzpJNOqnzooYfyjz322LqBAwdGAObMmeN+6623sr7++uul8evz8vJiTz311Oqrr7663wEHHBA45phj6gE2btxonH/++f0vv/zysr1VYAMltO2ROHSN3FQ3ualuSgY0rSsbjVkm1/pwlLWVAUrL61lbEWBDdQP14WijudYy0ZpN5y3KQtFYm6ZbXRO4HRpuQ8ftsEyqLkPH49BwO3U8Dh2v08Dj1PA6DXxOg60b19K3X/8W2khbD5mgtJTIRklKSqteSBMpTaoCYbbUhdlSF2rch7djYpY0aR07hwa47a1r6BqkuwvJ9uhkezSyvRrZ6TpZPXWy3YIerhD5VJNDFe5wBXpDfCtHs48JVGHEAogu5xCUCYLfboymJxfokmkQk2kXnb7kptZoGKrXdn5czVYscSasK9xNSa4Vv1h+/vln5wUXXNC/vLzcUVJSUvzXv/513bhx4wKJCXRffPHF9HvvvbcnwA033NB74cKFWx977LF1kydP7jts2LDhxcXFgX333TcQrw8Gg+snTpxY+84776y44oor+hQXFw9LSUmJFRUVBZ999tm1ABdeeGHvL774Ih2gpKSk+L333luRl5cXAxg9enTw5ZdfXnHjjTf2vummm2QwGNSmTJlS1loQwjPPPJN522239fJ6veb06dMz4iscAJx77rl9Ad55552sk046SX///fdXAUyfPn3tddddV3jUUUcVp6SkxAzDkJmZmdHnnntudc+ePZuZQSdNmlTTq1ev5bfffnvBzTffbBiGIaPRqDj33HPLtxfN+ktHJHMM/KUxZswYOWfOnE5fP3PmTMaOHdt9A9rJxExJzJSYUloLHkj7GMttK35uSssp1JQQNS3hLRQxCUZimBJ8Lp0UlwO3Q0fXBLqthUzci1Y0EF2ZQ9Mee0xKojF7M00iMZOt/hAbq4Jsrg2ytS5IWW3IFuqCbKkLUekP75QUyd2NzyHI9upkeTR7r5Pt1QnW1TKwZzrpeog0AqSJelJkAK+sxxOrxxGrR4/Wo4f96BE/WtiPCNchwn5E2A8hP0QbuhYgsqfh9LUQ8FLZXC/J71loRyfHy9NoHrGc2jkzshBNawnrzm3XFf6FaOm6+rkohJgrpRyTrG7BggWlo0aN2qu/nBV7LwsWLMgZNWpUv2R16ifhXkBcwNpT0TSBhsAAXC3esfnpHkYWWoJdxDRtgU4SjZlETUlDJMbm6gY21QTxh6JJvy810eRvmOh72PLclJKGsEkgHKUhHCMQjhGIxGgIxwja+4aIfRyJWYEZdllDJNYhX8D6iKS+JsramiSVP1fbBwJIsTcr2bPXIfA5NHxOgdfe+xwaPp9GSqZGmlsjz2fQ0ysoTIlS4I6QpoUxzDBa1A66iAbtfQOEA83PIw1NgQ2NaVXiKVWMJOV2nTAwhYYpdDDD6JFAkxAZ9lsJpJOdd4dwGa63NpqC6PIBtpPr2ZpUhx1lndpcwPNkQ0oPe8u39r4c61mltKJ8YxEgiSZTj89VPDF0QoRx4vrFzcqSnCOtX13xHICtHsvk5YkpZ1q+fgqFYrdE/XcqfhFomsCl6dsIdQD9sn2N2johLDHMEsq6T5CN929Ky8TbpNFs0l6GIjHK6kKU1Vh+h1vrQlTUh6mqD1NRH6YyYYt2winRlOAPS/zhWEfiTfA4BLleB7k+N3m+XPJSDfJSDApSDQp6OOiZatAj1YGjRb44U0oiMZP6sEl1Q4yacIyaBpOaYIzaUIyaUIy6oEldyKQ2ZOIPW5tTF+Sn6OSnGPRMNShId1CYatAjxYFDF+jC+pGhaQJDRhCh+gRBrs4S7EJ++zhhCycpD/tbeer2TqqVZ7BduQ6FZiWyTukBvjxIzQNfD0jJs4W7PCsaGCw/wK4MS8rGHxvbRUqIWYE2RMMQD7YxXODJAIevufavMQjESNha5jxUXx0Kxa5A/ecp9gri2rpd2r/HQY80NyMLraXz4j6I4Zhl6o3ETCJRSTgWoy4YbRTgKurDVPotAW/jps3ovkzqw1ECoZi1D8eoD0UJRTu3sktDRLK2JsramiiQPBjC0CDHa5Dt04nGoC4cwx+yBLYuZK5phkMT9EgxyLeFxjyfQX6qbgl2qdkUpOTh8lma0bgvpEwwfktspZItNAPIWBQRDSBClnlYhOrQwnVs2rCOflkO24Rchx62ykWozs5HaO/NDghX0kxYNWRR6+282eDJbBb9KoWGbHasIbH2CA2TbctELIxm2ls0hDDDdnR0CGLWMfZxm2i6vTRdesI+vXmZp0W5K5WdszazQqFIpMNCmxBiCFAopfxUCJEjpVR+BwpFJzB0DUMHD80d5WOmpcEKx0widj6/uFC3YkElRaOGJ+0vZkrLZGsLcoFwjEDIFurCUerDUWoCUbbWBdnqD7G1Lky5P9QuYS9qwmZ/lM3+HZc2KWJKNtRG2FCbPJ2NALJTnOSkuPA5rcAXr1PD69DxOjT7XOB1WKZha+/Ca7jwOrLwpoA3U2BosCGymrQBPZPcQ2DoAoeuoQuBQQhH2I8eqcOI1DetxRsot9bS9ZdZ+/ot7V95JInmzk7XvGswY+3XJjYiONRIgZpT4ZTHdtTIFApFC9ottAkhUoFXgeOB1cBA4F4hRC/gDCllMu8bhULRQSwfRCsyNxHTlKw1NPpm+wC6ZOaVUjYKh1WBMBtrgmyutgI6NtcEKasLsqU2xNa6EFv9IeqCrQtrbkPD5zZIcVmRwqn2carbIMVt7VPdDlLdBmluB/5glI3VDWyqDVJWaweP1AapbeMeYOl1yv1hyv1d83NzGhpe3STnpyoyPAYZHp0Mt06mWyPDLchwQaYLMt2Q4dbwOVIRRhrCEBgpAkO3dKqxeGBP3DQeDaM3lKMHtqIHtmIEttjHWxrL9IaKboj87ThScyINp713ITUnIhZEC9Vavowd7xFHtG7vCmhRKHYDOqJp+wsQBE4E/gAgpbxYCPEb4D7g8u4fnkKhiKPZwSTdEVQihKVRMnQNj9OgIMMLfZO3NU1JXTDKptoGymqCuBw66R4H6R5LEHMZOpqwzJYdESKltEzD8YjgqkCYDdUBNlQF2VDdYAt0llBXVhfstkhgK80NVG9tn+OfQxOk28Jdplsnwy1Icdq5+OxVRdyGsLd03EYGLudg3B6BO89aW9gdb6PFcIUq0cI1BCNNmtCGkKUJbYhECYZjNNgrjoQiURoiMUKRGOFIlFA0BqaJhiSEA2E4cbjcuFwuPC43Ho8Hr8dNisdNqtdNms9Lhs9FpteBU2/ldYmF0EO1lgAXqrG3WrRwDXr8OFRt72utsojtK+jJSt6nQqHYIXREaBsFjJVSxoQQN8ULpZSPCSE+7/6hKRSK3QFNE6R7HaR7HQzJT+u2foUQuIym4JF0r4N+OZYWUUpJJJ7aJWpFBjeEY2yoamCLP9Qk7EQsASduDm6wI3oTI3ybIn2tso7GeERMSXl9hPL6tlchaS+W7OTtHl/AsL0lXbw4AjQZQFJdOpleg0yvgyyvA69Dx9CtdYwdWiqGloZD64VDs3wYHQYYTnCkg0OTGAKcusTQBE5iVG/dxNgRB3RwrROFQtEVOiK0CSlbXden+z7JFQrFXo8QAqchcKI1rcIG9M7yAtaqIHHzbjRmCXUxUzamfGkt/6SUkmDUZOHc78noN5yqQJiahgjVgQjVgTDV8eOGCDV2WbCTAR6t0VVhzWlYfnx1oWiH0sjUhWLUhWKsrerAyhtt4uYUs4JHzlZim0Kxs+iI0KYLIQ6QUn6fWCiEOA3rJ51CoVDsFBy6hkNnG7+/OPE8fcmEOk3TyPZoFOWntuteDZGYJcA1hG3hLkIgHGtKPp2wD0aspNTxfShiWubNqLWucEOkSdPnNjRS3Q7SPJbPX1oz3z+DNI8jobypjct+ZlNK6hqiVAastDFVgTCVgUjTcX2YqvoIVQHrvDuWtmuJQ992aTiFQrHj6IjQdhfwlRDiE2CQEOI5YBiwH3BSezoQQvQBHsXKclkAXCelXNWizadAYnicD/iTlPI+IcTFwHHAOmAE8KGU8uEOPINCodgLiEfmtibUrTE0+mX77DQhMnm6kIRzmd28XcxO4Bz3yTM7sLJMNGZiSktj1hU00WS27m+blVvDlJLahoglyAUsQS4YMa20M/Fk1LGmBNWRZOcJQnDUNPHX1W33vr90Pvjgg5SpU6f2WrhwoW/IkCENqampserqar1nz57hxx57bN3IkSM7rdYcO3Zs0aRJkyouv/zyqu4cs2LPpt1Cm5TyfSHEKcCtQBZwBjAXOEFK+Uk7u3kCeFpK+Y4QYjzwNHB0izaLpZRHxU+EEG9iRa0C5AGXSylrhBA9gDIhxItSyq3tfQ6FQqGApsCO7ki2EdfsxYW4xrx7sW1NtUYXtFNCCDR7pQ5JU/Lm7aEJQYbXSYbXud227WXNojmMG1vUbf3tiYwfP95fVFS0asiQISMffvjhtccdd5w/Fotx8sknD5gwYULRsmXLFrVcUL29vPnmm6vT09M7u2hyM4QQo3/++ecf9+aF1n8pdCTlxz7AeinlYZ25kRAiG0tLdrpd9DHwthCiQEq5Md5OSnl1wjV9AFNKudau+0tCl4OBtUBtZ8ajUCgU3UVbmr14EuVErVXMtFY00ESTIBZfuzceiRuPxtXiK0S0EZmbuCJH4hrDEnvfyhrDccHPNGWjZrG9gqAiObquc+6551acd955RfPnz3cfcMABwc70k52d3S0Cm+KXRUfMo/OBV4BJnbxXX8AvpQwCSCnDQogqoB+wsZVrpgBPJhYIIdKBR4DRWPnhkqqfhRCXY6chycvLY+bMmZ0cNvj9/i5dr1Bz2F2oeewe1Dy2H9n4J76z/gbq63faHPb7/Qejd8qNEii9Z/zczl4biUQEwNSpUwt/+OGHlAkTJlTW1NToy5cvd//4448+KeXcNWvWOKZMmdJ769atjlgsJg466KC6hx56aKPb7ZZXXXVV4auvvppzwQUXbH3wwQc3Anz66ae+3//+970AotGouO666zZfeOGF1QCxWIzbbrst7/333890u91mJBIRF1988dZLLrmk6vDDDx8McOaZZw5wuVzy//7v/zb9+te/rl24cKHr6quv7l1fX69Ho1ExYcKEqttvv71M0zQmTJjQ//PPP0+fMmVK2aJFizwrV650+/1+fcOGDT92eWIVXaIjQtssKWVnBbYOI4RwAYdKKacmlttJfCfbWrhvhBBHSymXtrxeSvk0lvmVMWPGyLFjx3Z6LDNnzqQr1yvUHHYXah67BzWPXUfNYXJqa2u1p556qseIESMCH3744cozzzyz32effZb+/fffL8nNzY2dc845fWOxGOPHjy867rjjqh944IFVoVBIjB07dtDVV19d+Mwzz6x//PHHN2zevLnRrrp69WrHySefPHj69OkrJ06cWLt69WrHPvvsM3zIkCFLDzzwwIa77rqrxyuvvJLz/fffL8nMzDQ///xz75VXXtnvqquuqpw9e/ZSIcTo1157bVXcPFpbW6sdd9xxg6dOnbrx6quvrqiqqtIOOOCAoS6XS95yyy1b3n333dUlJSXFn332WdpXX321zO12y/POO0+FCe8GdMS5olQI4U5WIYR4oh3XrwFS4n0IIZxAJlDaSvszgNdb3Cc9fmybTOfTziAIhUKhUCh2FNddd12fkpKS4kMOOaS4X79+oX//+9/Ldd0yl48bN64mNzc3BvDyyy+v+eKLL3yLFi3yXnvttVsBXC6XnDx5cvkLL7yQa5rbpph55plnsnNzcyMTJ06sBejfv3/k4IMPrnvyySdzAKZNm5Z7xhlnVGRmZpr2/QL333//2tbG+uqrr6ZXV1cbV1xxRQVAZmamecYZZ1Q8++yzuYntJkyYUOX1eqWmabz00kut9qfYeXRE0/Yh8K4Q4gWs6M1Ee/uB27tYSlkhhPgIy6/tHeAY4Gsp5UYhxKnA5y2WwroAmNiim3dtzVpECGEAxcA/OvAMCoVCodjD6IqpcmcRD0RIVpeZmdnMP23VqlVOgMLCwsa123r27BkJBoPa5s2bjYKCgmZruq1du9ZZVVVllJSUFMfLqqur9aysrCjA5s2bnXl5ec1Sb40fPz7pWADWrFnjzMjIiBpGkwiQl5cX2bRpU7NIlZbjVux6OiK0TbP3LaM9gXavLnMl8IgQ4gSgkKalr+7E8l/7GkAIsT+wXErZMsjgG+A1IcRqoD/wgpTynfY/gkKhUCgUu5b+/fuHAdavX+/o27dvBGDTpk0Ot9tt5ufnb7MIb58+fcK9evUKzZ49u9EVqKGhQQQCAQ0gPz8/XFZW1ixM9bPPPvMdeeSRSddq69OnT7i6utqIRqPEBbeysjJHz549VXTpbk5HzKOzsASlltsAYHZ7OpBSlkopT5FSTpFSniylXGGXj5BSfp3Qbp6U8qok10+VUv5aSvk7e/+nDoxfoVAoFIpdztixY+uHDRsWeOihh3IBQqGQeO6553ImTZpUrmnbfi1fdtllFevWrXN9+umnPgDTNLnwwgv7TJ8+PRPgoosu2vr6669nV1VVaWDlj7v11lsL49f7fD6zpqZG/+STT3xTpkzpdfbZZ9ekpaXFHnvssWyAqqoq7fXXX8++6KKLVPqs3ZyOaNrullKuSVYhhJiarFyhUCgUil8q8eS6YPm0HX/88dUPPfRQYzaE3/72twVffPFFusvlMpctW+Z+//33V4GVFuSDDz5YMWXKlN6jR48ujkaj4qCDDvI//PDDG5Ldp3///pEZM2Ysnzp1aqFpmsI0TcaOHVt77bXXlgPccsstW8LhsBg7duxgt9ttejwec/r06avj119++eVl5513Xn+n0ynvv//+dWlpaeaHH3647Jprrun9wgsv5EQiEXHWWWdVTJ06dQvApEmT+i5ZssTz0EMP5c+fP9/7z3/+c92OnEdF+xEdzccjhPBgrYQAViLchm4fVTczZswYOWfOnE5fr6Kkuo6aw+5BzWP3oOax63R1DoUQc6WUY5LVLViwoHTUqFHlne78F8Bpp53Wb+DAgaH77rtv064ei2LnsmDBgpxRo0b1S1bXodTcQojbga1Y5tDZwFa7TKFQKBQKRRe45557ct988800gJUrV7oHDx7cqcS8il8uHVkR4XqswIFHgbgz5BDgciFErZTywR0wPoVCoVAo9gqGDh0avPnmm3vfeeedZlFRUfCiiy5S644qmtERn7YLgAOllM1s20KIvwMfAEpoUygUCoWik5xyyil1p5xyyuJdPQ7F7ktHzKP1LQU2ALssaVixQqFQKBQKhaJ76IjQliKEKGhZKIToDfi6b0gKhUKhUCgUipZ0xDz6AvC9EOKfwHK7rBiYDDzQzeNSKBQKhUKhUCTQbqFNSnmvECIDuB6Ir0EaBO5XQQgKhUKhUCgUO5aOaNqQUk4VQtxF8zxtyp9NoVAoFAqFYgfTbp82IYQmhEgDYlLK76WU3wPpQogO5XpTKBQKheKXwAcffJAyatSoIUKI0UOHDh1WUlJSPHjw4GFHHHFE0Y8//ujqav9jx44tevrppzO7Y6w7mrq6Ou2KK64orKmp6bRMMGfOHLcQYvS7776b2la7uXPnuuPz3tl7dZRDDz10kMvl2v/9999vHNvgwYOHffbZZ40+/YFAQJxwwgkD9t9//yFDhw4d9uSTT2Zt3LjR+NWvfjVozJgxxUOGDBn2wgsvZPzrX//K6Ow4OjK5U4Fy4OaEsjOAJUKIAZ0dgEKhUCgUeyLjx4/3v/baa6sAHn744bWzZ89eumTJksU+n8+cMGFCUSQS6VL/b7755uqLL764W3K1CSFGL1261NkdfSXjrLPO6jdu3Li69PR0s7N9PPvss9np6emx6dOnZ7fVbvTo0cH4vO8svvnmm+U5OTnNXtCvvvpq6dixYxutjc8880xWVVWVMW/evJ9nzJixwufzmffdd1+Pnj17RubMmbP06aefLs3JyYm+9tprWS+//HJ6Z8bREaHtVOAwKeUd8QIp5SPAFcDDnbm5QqFQKBS/JHRd59xzz60oLS11z58/3739K1onOzs7Zhgd8mLaJbz66qvp1dXVxplnnlnbWpvtacVisRhz585NueGGGzZ++OGHmXV1dbu9FS8vLy+maU3DXLt2rbOwsDAMUFxcHD7//POr161b5+zdu3cY4PDDDw8cf/zx/gceeGD9tdde2zcajXb4nh2ZlDop5ayWhVLKz4CUDt9ZoVAoFIpfIJFIRAA4nU552WWX9crJyRl18cUX9z799NP77bPPPo1mvTVr1jhOOOGEAWPGjCneb7/9hlxxxRWFwWBQAFx11VWFOTk5o373u981ptr69NNPfQcccEDxAQccULzffvsNSTSzxWIxbr311rx99913yEEHHTR49OjRxY8//nhWMBgUJSUlxQBnnnnmgJKSkuK33347DWDhwoWuI444omjMmDHF++6775A//vGPeaZpKcomTJjQPzU1dd8bb7yx54knnjiguLh4WGFh4chkz/viiy9mHXHEEa0KbO3h3XffTRs3blzNpZdeWhEOh8WLL76YkVhfWVmpnXzyyf0HDx48bOzYsUXvv/9+WmL9jz/+6DrqqKMGHnLIIYMOOuigwWeffXZfv98vAB599NHs/v37Dx89enTxRRdd1PuAAw4oHjRo0PAHHnggp7XxmKbJDTfc0HPgwIHDDznkkEF33HFHj8T6SZMm9U18ff7whz/kvfLKKzlffPFFeklJSfH999+fc9lll/WaOXNm+quvvppdUlJS/OKLL6YDDBkyJOx2u8333nsvLdm926IjInxWG3VtqjIVCoVCoeg0t6fvNN+lpnvWzO3MZbW1tdpTTz3VY8SIEYERI0aEnnnmmfWVlZXGZ599lv79998vyc3NjZ1zzjl9Y7EY48ePLzruuOOqH3jggVWhUEiMHTt20NVXX134zDPPrH/88cc3bN682RHvd/Xq1Y6TTz558PTp01dOnDixdvXq1Y599tln+JAhQ5YeeOCBDXfddVePV155Jef7779fkpmZaX7++efeK6+8st9VV11VOXv27KVCiNGvvfbaquLi4nB8nMcdd9zgqVOnbrz66qsrqqqqtAMOOGCoy+WSt9xyy5Z33313dUlJSfFnn32W9tVXXy1zu93yvPPO65PsmRcsWOA79dRTqzs1zzYvvPBC1r333rshPz8/dvTRR1e/+OKL2VOmTKmM11922WV9ampqjMWLFy82DIOLL764d+L1dXV12lVXXbX1tNNOqwW47rrrCu644478++67b9M111xTAfC73/2u37333rv+qKOOWvfjjz+6xowZM3zEiBHB4447zt9yPI899lj29OnTcxcsWLC4Z8+e0X/84x+ZW7ZsaXw9XnrppTWnn366iJ//+c9/Lquvr9fXrFnjfPPNN0vj5ZWVlUbfvn3DDz744MbE/vv37x+cNWuW99e//nWHhN2OaNp+EkL8UwgxQDRRJISYBvzUkZsqFAqFQvFL4rrrrutTUlJSfMghhxT369cv9O9//3u5ruuN9ePGjavJzc2NAbz88strvvjiC9+iRYu811577VYAl8slJ0+eXP7CCy/kxrVdiTzzzDPZubm5kYkTJ9YC9O/fP3LwwQfXPfnkkzkA06ZNyz3jjDMqMjMzTft+gfvvv39ta+ONmzSvuOKKCoDMzEzzjDPOqHj22WdzE9tNmDChyuv1Sk3TeOmll5L2V15e7khPT48llk2dOjW/pKSkOL4BJJ4/+uijjcqempoaze/36wMHDowATJ48ufy7775LW7t2rQEQjUZ57733ss4///zyuLl48uTJFYn3Gzp0aOg///lP2v777z+kpKSk+J133sn8/vvvmyX+HzBgQMNRRx1VDzBy5MhQSUlJ3bRp05IqnV555ZXsY445prpnz55RgEsvvbTK6XTK1uazo6SmppqJQnl76Yim7XpgJlZi3fg7SgOWAeM6emOFQqFQKH4pPPzww2uTaWziZGZmNhNqVq1a5QQoLCxsdGzq2bNnJBgMaps3bzYKCgqaOTytXbvWWVVVZcQFIIDq6mo9KysrCrB582ZnXl5eM0f58ePHtzqeNWvWODMyMqKJPnN5eXmRTZs2NQtWaDnuZJimiRCiWdndd9+9+e67794cPxdCjJ49e/bSZNc///zzmStWrHDHn800TXRdl//85z+zb7/99rJNmzYZkUhE5OXlNc5J4jHAFVdc0XvJkiWer776allaWpr56KOPZr/wwgvNzJ8ZGRnNniUnJyeyadOmpILT5s2bHSUlJc3mLyMjo+NOaK0ghJCmaYrtt2xOR5LrbhZC7AdMAva3i+cCL0spGzp6Y0X7kFJiShNd07ffWKFQKH6JdNJUuTvTv3//MMD69esdffv2jQBs2rTJ4Xa7zfz8/G2Egz59+oR79eoVShR8GhoaRCAQ0ADy8/PDZWVlzQSQzz77zHfkkUcmzaXap0+fcHV1tRGNRokLbmVlZY6ePXuGO/os2dnZ0dra2k4HDrz11luZP/zww5K0tLRGFeO5557b55VXXsm+/fbby3r27Bl1OBxy8+bNjTJLWVlZM/llzpw5KSeccEJ1vI9wOLyNQFRdXd3si3Tr1q2OeJBAS/Lz8yNbtmxpdo/q6upuiwqpq6vThwwZEuzodR2aZCllg5TyWSnlVfb2TyWwtY40TWJ1dUSrqpCthH7HzBjhWJhAJEBtuJbKYCVbA1vZ5N/Eurp1rKldw7q6dWzyb6ImVEPU7DZBX6FQKBS7iLFjx9YPGzYs8NBDD+UChEIh8dxzz+VMmjSpPDEiMc5ll11WsW7dOtenn37qA0sbdeGFF/aZPn16JsBFF1209fXXX8+uqqrSwMohd+uttxbGr/f5fGZNTY3+ySef+KZMmdLr7LPPrklLS4s99thj2QBVVVXa66+/nn3RRRdt7eizjBgxon7VqlWdyku3cuVKh8fjMRMFNoCzzz67aunSpZ7Zs2d7DMNgwoQJldOnT8+JR1w+88wzzbRoAwcODM6aNSslGo0Si8X4z3/+s01KjdLSUnd8/hYsWOCaPXt2aksza5xzzjmn4uOPP87YuHGjYd8vs6GhodsiWteuXevaf//9Ax29rt1SoxBiEHAw8LGUcpMQ4iLgWix/tt9IKas7evNfKmYgQMzvx6yvJyqjmKZJdOtGpNtJ1OvCdDuJESNmxjBl+1LahGIhQrEQVcEq3IYbn8OH1/AqDZxCoVDsIj744IOUqVOn9gLLp+3444+vfuihh5o5nP/2t78t+OKLL9JdLpe5bNky9/vvv78KrNQgH3zwwYopU6b0Hj16dHE0GhUHHXSQ/+GHH96Q7F79+/ePzJgxY/nUqVMLTdMUpmkyduzY2muvvbYc4JZbbtkSDofF2LFjB7vdbtPj8ZjTp09fHb/+8ssvLzvvvPP6O51Oef/9969LS0szP/zww2XXXHNN7xdeeCEnEomIs846q2Lq1KlbwIqOXLJkieehhx7Knz9/vvef//znutbm4eyzz6587rnncoCyjszfggULXKeffnpRKBQSTz75ZFY88KCmpka74447CgDOPPPMgffcc8+6p556at3kyZP7DBs2bFhubm7k6KOPrgXLT+6NN95Y+eijj64777zz+g8dOnT4gAEDghkZGbHZs2enTpo0qe9LL720BmDUqFH1b7zxRsb//d//9aqoqDD+8pe/tGrSvuqqqypKS0udhx12WHFubm7k4IMP9vfs2TN8ww039A4Gg+vfeuutzPjr6vP5YgCvvvpqdigU0kpKSopfffXVVXfffXdevM3XX3+d+u233y41DIPS0lJHVVWVEQ+a6AhCyvb51QkhXsDSzN0IuIClwDtYgl+FlPKSdvTRB3gU2AwUANdJKVe1aPMpMDyhyAf8SUp5nxDiIaz0InXAEOA2e2WGNhkzZoycM2fO9h+yFWbOnMnYsWO3KY+aUWLSEr7CwXqidbVE/X6ikZBVLltxBdA0hM8LKT6Eo8N+iI0IIXDrtgDn8KLtwMUppJTb+Cx0hNbmUNEx1Dx2D2oeu05X51AIMVdKOSZZ3YIFC0pHjRpV3unOfyGcdtpp/QYOHBi67777Nu3qsbSFLUAOuuWWWza15de3K4n7uLXmV7czOeuss/qOGzeuLjE6NpEFCxbkjBo1ql+yuo7YZ/tLKQ8FEEL8EfhZSjlRWN/k/2tnH08AT0sp3xFCjAeeBo5u0WaxlPKo+IkQ4k3gVft0jJTyMLt8PPAyUNSBZ+gwcZ+yqmAVMRkjakYbhTUzGoX6ALI+AB3JfG2ayDo/1PmRLpclwPm8HRaKpJQ0RBtoiDYgggKv4cXn8OExPF0SsBr7N03MujpidX6QJnpmFnqKb/sX7oZIKUFKRBKzg0KhUOwu3HPPPbmDBg0KnX766bUrV650n3DCCTW7ekzbQ9M03nrrrVXXXHNN4UEHHRToyqoIv3Sef/75jAMOOKC+NYFte3REaEv0XTsVmA4gpZRCiO1K1kKIbOA44HS76GPgbSFEgZSyUZ0spbw64Zo+gCmljIcZH5HQ5WqgpxBCyPaqCztBXFCrCdXExweBBqgPYDYEgSYNVH2sAX80QERGiJpRIjJGVEaJyCiG0BmW0iRfflc9n5AZxpQxojJGDInpMIg5NYb1GMngzMEArKldwzcbvqE4q5gROSNw6cndBqSU1EfqqY/UowkNr6NJgOsoZjiMWVuLWV+PTAg9j27dglnnRs/KQnN1eVm9nYYZDBItrwAzhp6RgZaa2mGh1h/2Uxeuw2N48Dl9OLTOa0gVCoWiNYYOHRq8+eabe995551mUVFR8KKLLuqWZax2NDk5ObHWUoLsah599NHshx56KL+8vNwxYcKE/u++++7q7V+1Yzj//POru3J9R8yj3wAXAIOBd4EBUsp1QggX8KWU8sDtXL8/8JmUMiOhrAz4tZTy21auuRv4VEr5aZK6G4GeUsrftXLt5cDlAHl5eaNfeeWVdjzltlRFq/jbpr9hCpOojBKTUaJEbUErxmXpkyl2WQLW+/4P+TzwRdJ+MrUMbs1pWrb1D1v/RKCVGI4TUo7j6LSjQdNY2LCQf1X8CwCHcFDkKmKIewhD3EPIMVpN5tyIEAJNaAgEQggEbQgrpomMxaAd7wmhadDG8ioSiZSycd9Q34A3xYsudrIPXjTaTPAEQAiErkM7tG4SmdT3UBNa47Yz8fv9pKSoBUi6iprHrtPVORw3bpwyjyoUSegu8+i9wGL7mmdsge1g4EFgXpdH2QJbGDxUSjk1Sd1+WFq3ia1dL6V8Gsv8ypgxY2RnfS/W16xl64zWg2l65HgoSs8AoG9ZNrnRLBzCwCEMDKFjaA4cQifDkUZRr4zG6w6N7U/IDKEL3d40a4/G6PTBFKWlgNDwMIzatAksqPyJVTWrWBJcwpLgEgB6p/bm0XGPYmgdi0LWNR2H5sDQDAxToAdC6PUhdBPakulaIjQNM9VHLMVDVEYJx8JEzAgRM0IgEuDrDV/TP70/RRlFLJuzjOzhVg7DXqm9SHWmtqo17A5idXXEqqosIbQVNJfL0hq6t10eUEpJdaia2nAtbf2wEaL7zdJtoXyxugc1j11HzaFCsfPpSJ62d2xzZb6UcoFdvAb4PbCkHV2sAVKEEG4pZVAI4QQygdJW2p8BvN6yUAixL3AzcJaUssM5TjpKliuLG7OuY2B+JoYWF8YMHJq11xM0Lb/OO4Zf5x3TeC6lRK7bCPX1iIHNXe9+0/e87d9cmhSSxuTMEyDzBCrNeubX/8yc6h+ZX/UjKYavUWAzpcmDcx9kaNZQRueNJt+X32q3MTNGNBAAfz0y0ABYQolAoGu2sKnHn9OBrukIBFEzStiMEDHDxMwYkVgEs9YEw0BkpIPHzdKqpXy85mO+2vAVDdEGxvUex+9G/46QGeKO7+6gLlzHHYfcQWFKIS7dRaozFZ/D123CjhkOEysvxwyFtt82FMLctAnN68XIzEQ4rZySgUiAymBlu9KrJJqldaHjc/pIcaTg1J3bvba9RM1oozAck7FGE7ghDHRN3+naPoVCoVDsGjqkopFSlgFlQoh9pJQLbV+0jdu7zr62QgjxEZZf2zvAMcDXUsqNQohTgc+llIkOlxfQQpMmhDgA+C0w2Rb8bgBelVK2GorcVZyag56OnvTxZltCmJTEaF17I2MmcukKzNk/EJuzAMosLZ3jj79DjBzapbFkaT6OTB3NkamjifWKUR2pw1y3EZwOVjas44v1X/DFess8W5hSyOi80YzOG82I7BE4dadlJgw0WEEQSQInJJKoGSFKhDYecRtqGqqYueFDPq78jnUNTW+HuACZ5koDzdJKlQXKuPHLG/njQX9kSNYQQg1WGhOf00eqIxWH3jlfMWmaxKqridV03GfXDAQIBwJIn4cat0mD7HBuScDyf6wN1VIbqsWpO/E5fPgcvnZrQiOxSKNwFjEjjeeJptmYGWNroLnmN26mNTQDXejNjnVNb9LmqvQwCoVCsUfT2ey+DwNHduK6K4FHhBAnAIXYPmfAncAU4Gto9H9bLqVszGEihPACnwARoNTWzKQAb3TuEdqHrukYmkEPb49m5VJKTExMaSKlJFJeTu3zLxL8fg6ytq6xnUhNQevbG+c+IxsFvthXs9BGDEFkbpP7r/3jEjrZzgyQJoRC5Jk+ru57PvNqFjG/bgkb/BvY4N/AuyvfxaW5eLLkPrIiLpAm64ObSdG9pBsdd8hPxjtbPuXNsv8CkG6kcmTerzhm0HgG5RST7c7GoTtI1VP5+1F/5/+++j/mlM3hlm9u4eYxN1PSs6SZsOM23KQ50zpkajTr64lWViKjnU887A/7qfNvwkQi0lIgLbVLkabhWJhwLExVsAqP4SHFmYLX8AJsI5TFt9bMsKU1pfyw5QcWli8kVhvjguoLKMpo0tya0nofbk8zKIRAFzpO3YlTc1p73dlh83p7kVISMSOEY2FCsZA1J2YYh+Zo1LAqLaFCoVC0n85+Wnfqm15KWQqckqR8RIvzebTwk5NSBoDOSzndjFlbS3jdOjwjrKEbqRmUffEVRKMYBQX4Dj4Y38EH4RoyxHJ6t4ls2sS6x/4JmoZ37BF4TxmP1rc3UppWGhH7CzgmTUt71E6VV4rh4+jsQzg6+xBiMsbP9auYV7OIubWL2BjaQkZIB2FpbB4ufY7lgTWk6j76eAro4+5Jb09Pert70s9TSJrRunNxWaicTyq+o8DVg3HZVuzJ0dmHUNqwgWNyDuWA9JG4NCfp0UxSI240b9NbLN+Xz12H3sX9c+/n4zUfc9esu7hi3ys4vt/xjW2C0SDBaBBd00l1pJLiTGlVqJCRCNHKSsxAh5NKNxI2I1QHq4iYTZpHWVsH/npIT7Ny6XVRsG1MyyJEm/5xccobyslyZzUKNH9f8HeWVDZ5IPww8wdK8ks4Z8g5zYS37SGlJCqtlDUBmuZMFzoO3dFMmHNojg49t5SSsBluFNAisQhhM5z0ecOxMBUNFVQFq0hxppDqTFXRuAqFQtEOdsxP7F8okU2bqP/f/wh8+x3BxYvRvF76vvIyQtfRvF56XP87nP0H4OjTu9UvPBmL4T3oIALffUfgk08JfPIpnv33J/2000jZf79tr5MQw/IfC5n2l2EsjKT1L39d6AxPGcTwlEGcX3gqddH6ZhoNr+7Bq7mpi9WzyL+cRf7ljXWn9jiai3pZWVnKGrawZPl39N8QIxDy80HBZr4yrEjpfp5CxmaVIISgwN2DPxZdhUBYX8IOS4MXrapC+P0YmZmN/ef58rhu/+vIcefw8tKXeXz+4/T09WRU7qhmzxAzY1SHqqkJ1+AxPI2BC5rQLC1nTQ2x6up2CUHJMKVJTbiGQKQVgc80kVXVUOeH9DQrl14XaW2sgUiAH8t/ZP6W+fyw9Qc2+Dfw8NiHGZgxEIDDCg9rnKN5y+bxXcN3zN48m9mbZ3PNftdwTN9jkvbbXmIyRiwaIxhtchEVQuDQHI0CnEt34dSdjfMfNhO0Z3YASkdfC1OajRrW+GvsdXR9nhUKheKXSmeFtue6cxC7M5FNm0n54N+sX7qUcGlpU4Vh4CouJlZTg5GVBUBKOyKpnL16kf+HW4ls3EjNjHeo++gjGubNo2HePJwDB1L48EOIxFQaAnR0dEPHjR3lKCEsI4SjISJmhFAs1PrqC0Cq0Twh7p8GXYuUkopINWuDm1jXsNHaBzcx0NsHc91Gos++RMrKVRwcbNJAXQ1M6CH49rxR7D+oeU5kl+4iw5WxjVZMRiJEtmxp3AtdJwsn5/Q+hSwjjWU1KxiZOZzWkFISiAQahSstFEWrrsMwJYYwLN8tO3iivaa2QCRAbai2fVrMaBRZUYms8yMy0hHu7ol4DUaDvLn8TRZsXcDSqqXN/NY8hofN9ZsbhbaTB54MWIJUry29uORXl/D2irf5bO1nHJB/QON1/rCfFGf3pLGQUjYKZInomt7oEtCdxLWRhmaQ6kwlxZGifPAUewwrVqxw3HrrrQVLly71OBwOGQqFRI8ePSITJ06suuyyyyo1TSMajXLXXXf1mDFjRpbD4ZCxWAyXy2WefvrpVRdffHGlx+ORhx9++OAlS5Z4Ro4cWf/1118vj69Bunz5cue5557bf8mSJZ6hQ4c2vPjii6sHDRrUOedbxR5Np4Q2KeW/unsguyuxmmpS/vtfwoDwePCWlOA76CC8B4xB83V+dQBHQQE5V15B5vnnUfvvf1P77ns4evdqFNikaWL6/ehpadteLMApHDidTSalmIw1+gyFo7bmow1tHMEQWau2kLF8DSNXlCJ65GBccJN1b6qRi5ZiAP4MF6UFBgJB8cogfSolg/a9BOGxBEj52bf4cvJI378ErS3fKCkx6+utZwdSwlGOde3LsT32hY2bMRFUmXW4HV68Tp+VQ83QQbc3TQO/n2i9Jbwl+7TS0Rt9EOOO+PHjePRrTbiGUMyKLG2IBfHHAjTEQjSYQRpiQYJmiIZYCLfm5ODM/QBLI/T35f8kbEbQDAPN6bL2dgDAYQWHMTJ3JAArqlfw7cZv0WjK4xaP8Ex3pnN0X0vYdegO3l/1Pv6IH01oDM0ayr65+7Jvj30ZnDm4mfDr1J2kOFPwGT5WaivJ8mRx8YiLOW/oeY1RqlEzynUzr6NXai/OKT6H4qzi1l+LLhAzOxCh0gmiZpSqYBXVoWp8Dh9pzrRujcTtCvEVSALRAFEz2nbOw+0QT7uTqMFU7JksXrzYefjhhw+99dZb17/yyitrAKLRKL///e97Tpkypf/5559f5fV65amnnjqgvr5e++yzz5ZlZmaaAK+++mr6+eefP7BHjx7R888/v3r27NlLS0pKimfPnp1266235t99992bAQYNGhROqNvlyzApdh3KPLodXMXF+I8+iqKxY/GM3Afh7F7fGz01lcyzziLjtNMw65tMdYHvv2fLX+4h9eijSTtlAkaPpkAIIbTGcUgpIRJFA9wYuDHAdngPm2HCMkZEWDnUwnPnY/5vLnLFauT6Tc2S6IrCfLjgDOs4KwPj/65B69+b7MwMsu02MhJBrt2A8LgRCHy6h6oX36SqtpZqlwvPvvviPehAvCUljdrH1khzpBGKhgiblvhVHwtw+7KHMYTBHwZeSYYjibC6HWLY68CaTSJdqHwL699/k8wvFqKFY0SPGEPW+Alohfn8a8Pb/Kf8y6R99XUXNAptmtCYWTmLiEzu6N87pXej0FZaU8rry7bJVAOAIQwOLTwUj+FBFzoXDb+IdFc6I3NGbmMWbC19iCY08rx5bA1sbVZeWltKbbiWuWVzmVs2l/177M85Q85hSNaQdszc7oeUEn/Yjz/sx224LdOp0fGl3rqKKU1LUIsEaIg2bJNkubtw6I5mwSFOzak0jXsIU6ZM6Tt69Gj/ddddVxEvMwyDe++9d9OMGTOyhBBMmzYt86OPPspYvXr1wrjABnDWWWfVzJgxo6JlnzfccMOGe++9t+Coo46qO+qoo+p31rModn+6RWgTQjwmpfxNd/S1uyGEwD9hAt5Bg3bsfRwO9IymOIvQkp+RoRC1H3xA7QcfNGvrHjmSgnv/CoAMBik97XRao8dNN5I1bhwAFavLqPn8G6vC0NH69kEU9UMM6oco6t/sOn3/kUnHKAb2azSFauEo5ikTCPxvFqHlywnMmkVg1iwAXMWDybrkEjwjt+3H6gwyPZlsrd+KiYk/Wk9DLEhZuIKbl93P7QN/Q093j+TXbgdTmvzkX87M8v8x8c/f0KsuQeP48fdEPv4eMWoYfY/NJduTiUd34dFceHQ3bs2FR3PTw9Vc6LyyzyQEGhJrLVoTk5iUSJfBsKxhje0GZgzkvKHnJQSUNAWXxE298aXFju13bPMpEcKKNHWkNIuelbaW0qyrQ4bD6GUV5ABbwjWYQoKuM9DoyTOHPcaMtR/wQem/mbdlHvO2zGO/HvtxTvE5DM3uWrqZ7iJuVu2I8NUYnCJ0UpwppDhSOp0apj3EzBgN0QbqI/UEY8FuNwUnIxKzoonrI03fz7qmW5q4nRDpuycw8l8jR7dWd/3o69dMHjG5HOC5n57LeWDuA31ba/vjhT/OjR+fMuOUoatqVnlbq98eZWVl+tdff5324IMPlras0zSNFStWLAJ46aWXskaOHFlfWFi4zS+/Z599dq3WIlL97rvv3vzNN9+kXnDBBQMWLly4ODs7e8equBV7DN31CTBs+00UHSFr8oWkjBtLzdtvU//1N8iEvGqi5fJRbSwnRYLZJeWQQzAyMnANLsY5cACas8m01jINRTIfOV3oZLgycBu2b53bIHPSJDInTSJaUUFg1mwCs/5Hw/wFhJYuQ/M0fRY6f/6Z+spKPCNHonmtckMYZLgzqAxWkufK4d7im/jTisdY2bCOm5fdx60Dr2Kwr1+750wGQ0S++o6bMj5mtW6txZuxr2BYpZfYcYczPG8fnP/9Bvn1bOSCxRx37h84ob+V5FhK2aYgcWT2wa3f2K8htVpITaF/en/6p/dvvW0S4jndWvpxmeEwZl0dpt/fbCkuMxxGA3Klj/JABVE78jUVOD/9GE4ZfjDvlH3K+1s/54ctP7ChZh1PjbkX3XBY7xWXszGR8I4gHkRSFaqiKljF6Oz9EKEQNAT5x/Ln8ccCDE4rYnDOEPrlDMLhat/6uDEZoyZUQ02oBiEEhmbg1KxACYfusPYdjHqNEzWjBKKW72RiQMauJGbGCJiB5pG+mt4oxMXpjJk2vpLHjhR+9wYWLVrkllLSp0+fbRNfJrBy5Ur38OHDk0Y9eb3ebX4VaJrGq6++unrfffcddsEFF/T94IMPVnXXmBV7Nm0KbUKI9r5RWk+/vxciDAPhcqF5PI1LJMWqq60F2Dvwq93Zty+5111H7nXXtdpG83gY8N677erPNWgQriQaw7jfl4emL09Tmk1CnIygC500R1qryV6M7GzSTjyBtBNPwAwGaViwAOfAAY31Kf/5kLLVq0HXcQ8Zgme/ffHstx/uwYPxOrwEIgEyHGncOfh33Lv6GX6oXcytyx/ipv6XMia9FW0dUB2p5YcVX/Kr2QHMz7+B+gDHjS/grTFZHJFVwtjLD2Ro2iDSnLa5deQR1F6ymdrvvkX079PYT+SuhxHZWejHj0NLKG8X0kTW1DZGmrYnTYgudLwOLynOlGbLeUnTbNSqbW9VB13o9PDkUhGsaPTTA0gzUji/8BROyTuKd7d8Si9XPlpDCEmI2dUL+bxyFk7dictw43S6cTo9uBwePIaHU4tObexn/pb5RM1oM5OdQ3dQG64ly51FT19PABZVLOK1pa9RFayiKlRFTaimmT/li/s8QIptsl/bsJH5dUv4rOI7WG35Zg7w9WFwWhEH9CxhVMHoZilyWp1yKRu1Uy1JFODiAp1Tc27zmkRikUZBLXH+dmdiZowG0wra6CpVVOFz+Eh3pe82foNt0V4N2OQRk8vjWrft8c6p77RnNZ8Oc9lll/VasGCBb8OGDc4777xzfWd+SBQUFET/9a9/rTr55JOLH3jggZzrr79ercWq2K6mbRMQBdoS3gRwbBv1v3iEriPcbktIc7mSajGM3FxkRkanhLddgSY0XLqrU+uDam43vgMPbDyXUhIuLibV5SK0bBnBRYsILlpE1QsvIjwess4/H+O4Q4iaEby6m1sHXslja57n88pZ3LXySR4Y8nsGeHs39hcyw8yqnM+a2Z9Q9GUpB62UxPVQYlB/Dht6FMcNPwBd6KS70vE5EgJGBKRl5ZN24mn4I5bPVHTzJuSCxUjA/OxrRHER+gnj0Er2Rzg6oIxuR5qQxES7iR/kZjBoCWqBwLYL3LeBEIIcdw6VocptvsjTjBTOK2ieFnFdcBPfVidfKtirezgl/xhwuxCGwd8X/J1N9ZuStj2r+CzOG2ppKYPRIPO2NPUpEGQYaWQ60shypBORTYLVuQUTKKnfh2WBUpbVr2ZjaAs/+1fys38lWjTGPrIA6XSyWVbzRcVsinOHMThzcIeiYlsT5gytaVm2iBlhg39Du/vsKv6wn58rf2ZlzUpW1awiEAkwIH0AgzMHMyhzELme3J3urwc0LsHmdXjJcGXsEcLb7sSwYcOCQghKS0ubTdwzzzyzHsAwjNF1dXXagAEDghs3buzw5J5wwgn+66+/fsOtt97ae+zYsf7uGrdiz2V730iXAU8BF8s2pAwhxOfdOqrdHKHrCJcbzeNGc7vbbWoSDsceJ7x1B0II/CeewD6DBmHW19OwcCENP/xAww/ziaxfj5aeRpY7k62BrcR++BH5zWx+M3IohT08VPhM+nt62X5qy/i8YjbfVf/Axe/Uc+ZP1txFdUHgoBFknnQyWlF/nICGRqY7s8mUu82gsCIyHT78fXzUPno30Q8/Jfb5t8ilK4guXQEZ6ejHHI4+/uiO5WmLpwmprUNkpuNJycDr8OI1vM18kmQshun3E6vzIyNdiN4XkOXOojpU3cwnKhkHZowiz5Vj5VkzI9ZasjJCyIygC4GsrLLGZhiMTB1MT3cPwjJqpZix87GlOFLIdGVaJvuGIEWRLP4w8CoyHWlkOtJJN1LQRXJt2WBfPwb7+jHePq+L1rMisIZl9aWMSLW1wOEwC8vn8dLaV2GFVVTo7cngzMEMzh7C4MzBFGUUdTjiMmpGG1eN2FH/d6Y02Vy/mVU1q8jx5DQGgizYuoB7vr+nWdv5W+c3Hk87bho5nhzACmbJdGeS7tp5ucTjaXU8hocMd0anfqztjeTn58d+9atf1b733nsZv/vd71rVhJ1//vkVl1566YCNGzcaBQUFzfzazjvvvD4nnnhizaRJk5KuwfeXv/xl8zfffJN6zjnnDPB6vTsmEkaxx9Cm0CalXCyEeBtrFYMZbTR9vjsHtTuieX1oHrelUeuiP9DeKrwBaD6fvVqE5SMW3boVzedD0xyku9LZ+v0PmF98B198xwRA9C4gts9r0L83L/IOP6daK5ttGJFHcEMtxrHj8B5zFL701MZ76EIn25Pdriz7QghrSaWiffD/vwHUnf1rYl99R+zDz5HrNhJ79yP08VaaDhkzobwCvB7weto04zk1Jx7hwVNv4JCgZ7oaU6KYDQ3E6uqQgUC3vu4ZrgwMYVATbn391V7ufHq52+HNEI1yVcHZTee2yR+XE8IRZDCI3FQGQCoOxqSPaKWjtkk1fOyXNoz90pq7xfb1FHJy7jiWBdawKrCWDYFNbAhs4vMNX+DWXbw8/pVGoW3B1gUUphQ2Cj07GhloAE0g3G7W1q5lefVyVlWvatSixTWex/Q9plFoG5gxkBHZIxiQMYCB6QPxOrysqF7B8qrlVAQryHZnN/Z/75x7WVe3jjxvHoMyBzE4w9LGDcwY2BjE0uVnME0QYhvtXkO0gQZ/A27D3dx/VdEqTzzxxNrDDz98yN133507derUxoWB33777TTTNNE0jQsuuKB6xowZleedd16/t99+e1V6eroJ8PDDD2d/++23qY8//vj61vqP+7ftt99+w+rr6/eOLwpFq2zX9iOlfLAdbf7ZPcPZ/RCGgXA6ceR1LpKxzb73YuEtjpGb23jsc/gInDyeQM88zIVLkIuXWYLTOmsR+v93YD++m/QrxmaVUDAqF06V2whODs1Btie7VU1Pa2hCI82Zhi/HR934LOqPGYu5eCly85YmLVttLeHfTG26yOW0hDdbiHNNnkTaiFG4HW4i838iuGQxMZ/PEvh9XvSsbPS0VLS0NPSU7kmC25IUZwoCQfWm1ZibypCbtiA3b0FuKrP24QjGxJPQxx7SsY6jUWtt1/qdk32g2NefYp8V0BExo5Q2bGC5bVIVaGiby5FZGcScBn/67k+EzTDZ7myKs4opziymOKuYooyiLmuMomaU8oZytgS2sMW/mS2V6zg563B8ugfpcPDU8idYWPlTs2syXZkMzBjIoIwm/9F8Xz5/Oewvzdod1POgbe4XM2ONuenKAmWUBcr4esPXgKU9vnjExZxSdIo9LxEMYbRpVpWxGESiEI1CJGIJ3NEoxKxAI2kY1v+QYYDDsPaGToOUBKNB3IabdFd6twmLv0SGDx8emjVr1uKpU6cWjBw5cqjT6TTr6ur0vLy8yLRp01aed9551QCvv/566Z133tnjsMMOG+x2u2UsFmPQoEHBmTNnLk1PTzeDwaCIJ9ctKSkpvvvuu9cfffTR9QCFhYXRadOmrbriiiv67cpnVex6theI8DkggZuklHN2zpD2PrpbeBNCIBwOcDjQnHakoKZBsg/3JGWtfQVIsHK7maa1l9IaZ8LW6I8VLzPt7PntXHw9u2g4sYJcYicfa+WFW7YKc+FizKUr6ZPTmwEFJ7d6rVt3k+XO6pJvUDxCNtWZSu2+XhoiDU1O9ZEo5GZDoMHaQmEIhZFVlmYrU/PhsX2vaubNpeaNN5PfIzubvi80Kae3/u1vCKcLIzsLPSvb2mdnY2RlNUbatkRKSayigsjGjUQ2bERGI6SfbM2NV3NRduXvrdcpGZ4m7YmMRreNRt7NcGgGg3x9GeTry4m5R1iF0ShySzm1jgjDs4ezrGoZFcEKvt34Ld9u/BawXsvfl/y+UTgKxULbBCSEYiG2BraiCY2ClAIA1tSu4e8L/s6WwBYqGyoxaT6PB/iGMtDbByIR9vMMIiXTxYDMgQzMHcLAzCIy3Zl0Fl3Tueewe4iZMdbWrWVZ1TKWVy9nedVySmtL6eFt+vH48ZqPeXHJi4zIGcGIzGEMzxhCX1dPtKhpma4jkdbfA3HiwniSoBep6wQMgwZjIy6XjzRfFl5PmvVDth3BInsTRUVFkddee21NW210Xee2227bctttt21JVu92u2VbiXPHjx/vX7t27U+t1Sv2Drb3ae0GzgbKhBAXJFZIKafvsFHtpXRGeItrAoXTiXA4EU6HlU9tBzg1d6VHYRg4CwuJJUlhkYgmNLLcWWxt2Go9x/BitOHbz+7vdXjJdGZ2bZAJ6EIn05VJqiOVunAdgWgA0SMH19/vQRc6bsONOyIwGiKYgQBmoB5nn6aoU8+++yEMA7PeqjPrA5b/WlUVekLiYRmLUffhf1v9cs2+/HLSf30qAMElP5Pxr+dYX1tHZONGZMIXrZae3ii0CcPANXgwEkksLxt65iLyeyDy8yAUQhQ3LTIffXI6srwS/aRj0PYfiWincL27kBFxcHvfKch9UtlINUsrl/Jz1c8srVzK2tq1FKYUNrZ9btFzfLn+SwakD6CyupLa/9RSHaoGYGyvsVw/5nrA0tYurlgMWEEV2c5Mejiy6OHMJteZRYreFNhyWl5CDFZMQwQ1pBFDGF0TanRNb0wfcxzHAZaAqaEhw9aPhZVlS6gN1zYTVFN1H8NSihidPpzjcg7r0hiIxSAWQ4YgWB8gWLkVp+Yk1ZmKx6nWiFUodgXbE9oapJRrAIQQ8eRTF7IXrT26K0gmvGFrzxoFtPi2B33JCqcTIzsbmZmJWVdn+XVFto3yc+pO0pxp1IZr29VvujO929bcbImhGWS6M0kxU2iINuDW3U0Rdi6gldt6R++Pd/T+AAhNQ0tJQUtJsdJ51NY2CeNS0uOG64lWVBKrrGjaV1YRq6hAz8xo7DP088+4FyxsXMJLS0vDUVCAo7AAR0EB0jQb3w+FD1leDVEzSkVDBdEkqznIUBhz3o9Q5ye6aCmiZw/0E49GO+LgxmXKuoqUErmxDLnoZ7SjDmvU0ESeeRFz7gKE221p/jxu654eN6JvL4yTLWFIxmKYX88Cd0J9ViYiO0GbZZqIqhp6udz0KjiicamwQCTQzCdrg38DteHaZgEAutDJ9eQ2pYQBenh7cOchf6ZHxEtWxImjvQltTRNZWwe1fvB5IS3F0nh3ERmNQTCIMxiyfAltAf/KnmdyWtY4fvQvY5F/OT/WLaciUsWsmgWEZaRRaIuYEf699UtGpA6in6cXeheWzAqbYSqCFTjCtc1WVFEoFDuHdttFpJR3AAghxkop/7TjhqSI0yi8ZWfvUcLZ9hCahp6ejp6eTsxfj1lXixlsntA01ZlKKBZqM3+WQJDpztwp/jYOzYGjg0uYCYfT8mFLSWl8/TSXC5mSQrSyErOhAWEYpNgrVrSk0fRs4x4xnOoLzmfY/qNxFPRET01Nel0ihmaQ482hoqGCiNlcQBYuJ86/3UXss6+J/ecz5KYtRJ99CV6ZgX70YegnHYvISENHR9M0dGGt7SqEaFMDHKusJLLgJ6ILFxFZuAhZYSU6Ti0ahFFs+Xn5a/yYFVXNVseNHxujRuD5teW3ZTb4qX1s2jb30A7YF/20E9ESVvKQoRBs2mLlyktL2WZpsDsOvoOyQBlratdQvaqa0fuPJtOduY3/o94QZmQkz9I0derfTiLr6y3/P7cb0lIRbsu3zm248Tl8OHUnoViIcCxMMBpsjGoFO0ggGIJgCBkMQTR53lYhBD3dPejp7sGxOb9CSklZuJyf/MtJN5reG8sDa/jnhjcA8OkehvoGMiJ1MMNSBjLA07tdATst2e7axgqFYoewPaEtmbFpm/9UIcRFUsptP1kV3cIvSWBriZ7iQ0/xYYZCmLW1zUzCmW5rmasYSVZnQCfLk9VmXinhcFh589xuK/IRkIEAZkPDNkJid6J5fZaw5kkuTAqnE0d+PjF/PbGqSsunKFk7IZr5HLoGDSIIuDu4pFpcm9QyCS9Y0byOCSeinXwi5vc/EHr3Q6I/LyP2zn/pMf5U3L6Cdpvao1VVbLr590TWrWt+j/R0PKNGkebNxuWyNGTpN/4es95vvRaBAGbA2suGAHpGBl63ZUKORRyYR45rbCcDDYRLSzG/n4/5/XxS/jwVc2hRwgoeEllTA/UByMpoFJbi85nvyyffl8+yDcu2iTaV0ShUViO78b0hg0H0YARvSjpp2QW4fBmNdfEgCSkl0UA9wfoaQvW1hBr8RGKRbXzptocQgnxXLvmu3GblXs3NUdkHs6huOZvD5cyp/Yk5tZZrlCEMnhlxJ1kOK72IPxrAp3t2Sc44hUKxfbYntLX3p9T5gBLaFJ1Gc7nQcnORmZmW31tdHXoMMtwZVASbr6dsCINsT3aznGdCCMtc7HZbgprLldzB3ulEz8iwzG5xQaChoUMJbZMhdB0tNRU9NbXdjv16ig/N6yFWU4NZU7NDI4eFEOT4elBPCC0cbdSaNdMyjTsexh1PcOlSgj/+hKe3tXyjlJLyhx/BM3p/fIceioxGCS5aRMP8BcSqq+nxu99az5ORgVlXh3C7cY8ciWffUXj22w9n377b/PDQfF60duS+01NT6XHjjc3KopWV1Lw9g+CCBeTudzBC14maUfwrlhHt1YOwGUZGI8gtW8Hng8z0Nn/4SCmh1m+tarGdjzwpJYTDjT8C2sKtW1o1t26baCuqCNf60dPSEG430v7xIBsakFLiBJy4SbVN0xEzQigWJmKGCUfDSU3c7aGftxfX9LVckreGK1lUt5yf/MtZWr+KuliATKPJNDx12YPURusY7OvPEN8Ain39Gejtg1vlbVModgu29+2ynxDisxZl+yYr674hKfZmhGFgZGYiMzIw/X68tbUEY8HGpLEu3UWWOwtdN5oLaG53h7QDQtfRU1LQU1Isv6tgsEmIS+Jn1xqay4WWlo7m83ZKOyE0zXpen49oVRVmIOnyhJ1GCIHweNFTfAivF5cQxPx+YpWVVjqIJLiLi3EXNwV/BH/8kbqPPqLuo4/QbaGauHZQ08i+/DL0lBTLXHffvTjy83doRKqRlUX2JRc3Xy+2sprK63+Ps3dvMs48E+OQAwgRIdQQIhIsg4z0pAmSZTCErKxu1QQJtu/fTz9jzl2IOW8h+kGjMSaflXxsmgOf4cXj8CRNOyMjEaIVFUmu3Jb4MlzgA5eVuDccC1uJkaMhwmbHEzLnOrMYm30gY7OtFUvCZqRxDiNmhOpoDTVRP7NqFjCrZgFgpRrp5ynkzPwTODhzvw7fU6FQdB/t+WRt+U20IEmZQtGtCCHQbc1VTiCTSNkKHLqT7IwCtG5IcNzyXsLjscyZWVnIcLjJbJfEVCaEsAIL0tK6bRxWLsA8zPp6olVVHRIck6E5nY3BDy3TM+gpKWgeD7GqKksA2w6uwYPJvupKamfMILJhIwiBc+BAPPvui2ffUWjOJi2Ms1evLo27IyQKyZGNm9DT0wmvXs2Wv/4Vo6CAjDPPIPfIIzENjZA/RDjSQCjVg6kDSGRFleV7lgRZWY05byHmnIWYPy6BcJOAZK4oTTheTey1d/GeNoH0fQ/AZew4jZQmNCtqGTc4IRwLUxuubde6qVJK5PqNyEXLMBctxVy6AnQd48wJMO5QAIzaBqalXk1ZBvxsbmRZ/WqW1q+mtGEDqxqam7xnVs5GC3U+tYlCoegc2xPa5kspk3tJJ7C3LWOl2LnoXi89+w5H13ZObijhdKI7nejp6c3MqEQirQpC3YXm8+Hweq2o4Q6aTIWuW6tLpKZuV5gUuo6Rk4OWkkK0vKLNZbQ0t5v0k04i7cQTCa9ejZGbi56W1mr7XYFnn5H0+ec/qfvkE6pff53oxo2UP/wIVS+8SMbE00mbMAGvKRB1gpjXxcoYZMZcxFwOpDSJmla+MqlrmNKk4Z3/Ev33J439i/590MaMQhu9D6K/ldrFqTmJfPglkR9+wv/DT4SLisg4/TR8hx22U/KYOXUnOZ4cwrEwdeE6grFtf2BI0yT6yD8wf/oZapMI6AlmY3PWPKL/eJFs4NC0VH6V3wORl0csbyhlmYLsEU2pYhbU/swxntZzJioUih3D9oS2c9vZT7vaCSH6AI8Cm4EC4Dop5aoWbT4FhicU+YA/SSnvs+sn2H3cI6V8sp3jU+zh7CyBrSWJZtSddk8hLJNpSgrRyirMQOurEMS1hHpKCsLbcROt5nbjKCxol5AoNA3XwIEd6r87EInpbhwOEMISpBsamrdzOkg78QRSjzuW+i+/pOrV14isWUPD/AWkn2JFo0rTRPM3oCFwRQUN83+kYdZsAt9/T+a555J2wvEANBxxNDUVdXhLSnAfMBotOwtTmo2bU3diaAaxK39Dbd/+1L77HuEVK9jy13sxpk0j/dRfk3rcsa0mR+5OnLqTLHcWgTWrqJk3h/DKlRhTLrDmTdOs5cZq6yAzHc3OeyiGDrb8/FKbcs7hMBC9eiLLyqG2Dllbh1y2EgHkp/hwHXtqY9NLX62iavz2tbR7CytWrHDceuutBUuXLvU4HA4ZCoVEjx49IhMnTqy67LLLKrUdFEx28MEHD543b17Km2++ufykk05K+oKsXbvWuOaaa3qXlpa6XC6XGQgE9EGDBjXcfPPNZQceeGCzf6I5c+a4DzjggOHvvPPOsgkTJmzTn9/vF7fddlv+Z599lu50Os1oNCo0TWPChAlVV155ZUV2dnbsqquuKpwxY0ZWRUWFY5999mn24bV161Zj9erVi7p3FvYutrf26Mb2dNLedsATwNNSyneEEOOBp4GjW7RZLKU8Kn4ihHgTeNU+PgLIAkrbeT+FYo9FOBw48npgBgJEK6uaacOEw4memoLm83XZf6xRSPT5GlOR7ApaCmfNBLUWNGpBA4GmYBJb4BS6Tsq4cfiOOILArFkYeXmN1wUXLcL/5VdkrFjBmhUrrES18brFixuFNs8+++DZZ5/tjllPSyPznHNIP/10/J9+Rs1bbxFZv56Kp58mWllB9iWXdHVakiKlJLJ2HQ0/LiS4cCHBH38iVl3dWO88bQKRPMt8aVw6CVJTrATLbQj1+pG/Qj/yV1ZQTlU1cvNWa+mzsq3N4jNkzMT4YQny9HN2yLPtaSxevNh5+OGHD7311lvXv/LKK2sAotEov//973tOmTKl//nnn1/l9Xp3SJTRd999t6ywsHBkW23OPffc/oMHDw7OmDFjNViC19FHHz3oiy++SGkptD377LPZ6enpsenTp2e3FNoaGhrE4YcfXlxcXNzw7bffLvV4PBLgjTfeSJs0aVLR0KFDgxMnTqx9/PHHN7hcLvnGG29ktVzhoaSkZPuZ0hVtstPWrxFCZAPHAafbRR8DbwshChKFPinl1QnX9AFMKeVau+4L4AshxOSdNW6FYlejeb04PB7MmhqEruMoKEBrR/RiR2lKRdJ2oEKX79NymbUEAa1D/eh6o99joxm73kobIqVEaBq+gw9udk3Vy6/QMHcubiw5xFU8GO+BB+ItKcE5YECnn0lzOkk74XhSjzuWwOzvqXn7bdJOOqmxvuHHH9FTUnD2799GL82R4QjR8q1EysqIlpWhp6biO9TyPwv9/DMbf3d9s/Z6VhbufUbiGbkPvh59Mb1u6sJ1NAzumHZUaBpkZyGys6CV1Ugcf7oRMz29Q/3+UpkyZUrf0aNH+6+77rrGCBPDMLj33ns3zZgxI2tXp0+ZO3duylVXXdW4dFZKSoq87bbbNkYikWYDi8VizJ07N+WGG27YeM899xTW1dWtTU1NbQyrv/POO/PWr1/v/Pbbb5e63e5GIXTixIm1b7zxRoXD4diuYPrXv/513fbaKNpmZy462BfwSymDAFLKsBCiCugHtKapmwJ0ygQqhLgcuBwgLy+PmTNndqYbAPx+f5euV6g57C78DQ18+d13O+dm0WiXU6EAVq4521QXP97hmKY19hbjdxx+OO70NPzZ2YhRozDjvnmmCStWdM+9s7Pg0ksora2F2lowTXIeeBCjrIzQ0KHUHzmO8ODBEI2hV1USy8qyFmoHfJ98iuvHH9ErK9FqaxEJ5urQoEFU9WhaezQnJ4dInz6EBxURHjSIWG5u09xu3tzYTkqJKWOYshteyzjebEL1gb3+f7qsrEz/+uuv0x588MHSlnWaprFixYpFGzduNI444oiB33//fcpDDz1U+u6772YuXrzYe+ihh9b+7W9/W////t//611RUeGQUpKVlRX9xz/+sbZnz57RN954I+3WW2/tVVdXp59zzjnl3377bWpZWZnjtNNOq/zrX/+6KdHkOm/ePM+jjz7ao7S01FVUVBR8++23V7tcLgnQu3fv0COPPJJ30EEHBfr16xcBOOGEE/wtx/vuu++mjRs3rubSSy+t+NOf/tTrxRdfzJgyZUplvP7NN9/MOvTQQ2sTBbY4cQ1jayxdutQ5derUgjfffHObeVJ0jN12pWghhAs4VEo5tTPXSymfxjK/MmbMGDl27NhOj2XmzJl05XqFmsPuYmfPoxkMbjdQIZHEfHnC6UJzt5IvbychpUQGAsTq6y0T6qBBcOwxfLt8OQd3MElxZzGDQSpLSqj76CNcS5bgWrLEWtLMb31vFj7+GC5by7f1ww+pW73aulDTMHJzMfLyMPLyyC4ayNDEMT/fseWfo2a0cR3d7mBNeXinvheXDBk6urW6HjfeuCb7kovLASqe/WfOlvvu69ta26E/L5kbP145/qSh4ZUrva3Vb49Fixa5pZT06dOn1XDvgoKC6OzZs5cKIUYvXrzY89lnn61YunSpc9q0aVn19fXaySefXH355ZdXATz88MPZ1157beFrr722ZuLEibVut3vdhAkTBg8ZMiR4//33b9qwYYMxatSo4f379w9deeWVjQLVvHnzfB9++OHKWCzG4MGDh7/00ksZF110URXAE088sebcc88dUFRUtM+BBx5Ye/LJJ1dfeumllTk5Oc1U6S+88ELWvffeuyE/Pz929NFHV7/44ovZiUJbaWmp++ijj65p79xs2bLFGTeHhkIhUVRUtOMymu9F7MxP0zVAihDCLaUMCiGcQCat+6edAby+swanUCi2ZXuBCkLXES5Xp/Pl7WiEEAifD83nswS4eCSwpqF5fYC9VFjCZj1iQhkk1HXcNUlzu8m58goyzzuX2g8+oPbd9yz/M1sok4Emt6L0k04i5YixGHk9MHJyujUKNb6ObqqZSl2kjpgZQyDQhAbCSikiEGhYGlHNrhNCIISGhrDS3WCVrS0v7bax/dK47LLLei1YsMC3YcMG55133rk+LkBdeOGFFQDFxcXhe+65Z3MoFBJLly51jx49uljTNOrq6vRoNNrsH8jr9ZqXXnppFUBhYWH02GOPrXrhhReyE4W2008/vUrTNDRNY/jw4Q3Lli1r9J848sgj69euXfvja6+9lv7WW29l3nXXXYV/+ctfCl9//fXlxx57bD1ATU2N5vf79YEDB0YAJk+eXD5p0qRBa9euNfr06dNqVufHH38861//+ldueXm5cfTRR9c8/fTT6+N1PXr0CMd92uKatu6Y272dnSa0SSkrhBAfYfm1vQMcA3wtpdwohDgV+FxKmSjFXwBM3FnjUygUyWkWqFBVjdBEU2LjbsyXt6MRQiC8XjSvF2EYOPJ6bP+iJDRq72q3XTO3LeJBCxkTJxKrrkHPytxGKOuIz1tnMTSDTNeel2OtvRqw7EsuLo9r3bbHwA/eX9KVMQ0bNiwohKC0tLTZP8IzzzyzHsAwjNF1dXWNdsyW2q0//vGPea+88krO3LlzF+fn58fef//91P/3//5fv8Q26enpzYSmHj16RGfNmtVs4eHMzMzGfl0ulxkKhZqFqzocDs4999yac889t6ampkY78cQTB9566629jj322KUAzz//fOaKFSvccc2YaZroui7/+c9/Zt9+++1lAH379g2uW7eu2XNeddVVlVdddVXlIYccMriioqJVeaK4uDisTKPdw85e1PJK4GIhxJNY/mqX2+V3Ao0RMEKI/YHlUsraxIuFEDlCiMeAQcBZQojm69soFIodhpUAuIeVpy01dY8S2LoTIYSVT69nTysoxOvb/kWJ1zscGLndq0VT7Bry8/Njv/rVr2rfe++9jM5cP2vWrJTRo0f78/PzYwDhcHgbNXV1dXUzYWjLli2O/Pz8di+HceSRRxYlnqenp5snnXRSdW1tbeMb8K233sr84YcflsyePXvp7Nmzl86ZM2fpxIkTy1955ZXseJuJEydWfvXVV2l+v7/TqvQPPvhg5+VO+oWyU4U2KWWplPIUKeUUKeXJUsoVdvkIKeXXCe3mSSmvSnJ9uZTyN1LKQinluHjuNoVCodgVaC4XjrweOHv1stYU3UH5uLqLeN5BR14ejvx8jJwc9PR0NK/PiuTdjUzbewpPPPHE2oULF/ruvvvu3MTyt99+O800TdrK0TZo0KDgwoULfXFB6J133tkmJDccDot//OMfmQAbNmww/vvf/2ace+657VsLDVi6dKnniSeeyIqf+/1+8d5772WOHTu2FmDlypUOj8djpqWlNYtUOfvss6uWLl3qmT17tgfg1ltvLevXr1/ovPPO65couP3www/ujRs3OtuTi+6OO+4obO+4FcnZbQMRFAqFYk9BOBwY2dnWmrl1dcTq6pDRzi3w3t0ITUPzeq2cfh7PdgUzGY02bZGItaRa/HwHpYHZkxk+fHho1qxZi6dOnVowcuTIoU6n06yrq9Pz8vIi06ZNW/nrX/+6Nm52PPPMMwdccsklW6+55poKgDvvvHPTypUr3cOHDx8+ePDghvz8/Eh5ebnjqKOOGvjpp5+uBOjRo0dk3bp1zkMOOWTw5s2bHRdddNGWeIDAUUcdNbC8vNxxww039Ha73Ws+/PDDtC+++CLd5XKZeXl5kZtvvnnrjTfeuPH555/P+cc//pGr6zr19fXaEUccUfvAAw9sXLBggev0008vCoVC4sknn8yK91tTU6PdcccdBfaYB95zzz3rzj777Jovv/xy6R/+8IeeBx988BC32236/X7d6XSal1566Zarr766HGDq1Kn577zzTmZ5ebnj+OOPb5ZHZ/ny5e6d98r8MhGdcazd0xgzZoycM2dOp69XkY9dR81h96DmsXvY0fMopcSsrydWXdPuyNvuRGiatVKGz9eplTJaQ8ZitjAX5as533dpDoUQc6WUY5LVLViwoHTUqFHt8kv7JRP3cduwYcOPu3osip3HggULckaNGtUvWZ3StCkUCkU3I4RoXP7MjAct7OCVJnaUoNbsHrpu+eLtgOTOCoVi++zeDhgKhUKxh6N5vTjy83EUFlprxHajMCWEQPP6MHJ74OjTB0ePHpYZVPmm7fG88cYbaTfccEPv8vJyx8EHHzx4V49HsXugNG0KhUKxE9CcTrTcXGRmpqV1a5EHrpmrSov8cC3bCCEQHi+a17PbBz8oOsfEiRNrJ06cuHhXj0Oxe6GENoVCodiJCMNAT03dfkOFQqFogfqJplAoFIrdDdM0TWXjVex12Nr0VhcKVkKbQqFQKHYrhBCbGxoaVHoIxV5HOBx2CCFaXeNVCW0KhUKh2K2IRqN3lJaWOuvr6z1K46bYWzBNU2zcuDElFos911qbvSJPmxBiK9aC9Z0lB9jrcwZ1ETWH3YOax+5BzWPX6eoc9pVS5rZWOW/evOMMw7hNSpmPUjAo9g5iwNfRaPSy0aNHJ03wuFcIbV1FCDGntSSQivah5rB7UPPYPah57DpqDhWKnY/69aJQKBQKhUKxB6CENoVCoVAoFIo9ACW0tY+nd/UAfgGoOewe1Dx2D2oeu46aQ4ViJ6N82hQKhUKhUCj2AJSmTaFQKBQKhWIPQAltCoVCoVAoFHsASmhrAyFEHyHEDCHEk0KId4UQA3b1mPZEhBCfCiE2J2w37Oox7e4IIQwhxA1CiHohxJCE8gwhxCtCiKeEEB8IIVTKhTZoYx6fa/GefGRXjnN3RghRIIR4XgjxkBDiCSHEa0KIXLtOfUYqFDsRtWB82zwBPC2lfEcIMR7L8fboXTymPZF1UsqjdvUg9jAuBr4FvC3K7wK+k1I+IoQYAbwlhCiWyjm1NVqbR+ykrYrtMxgolVL+AUAIcR/wF+BS1GekQrFTUYEIrSCEyAbKgBQpZVAI4QT8QD8p5cZdO7o9CyHEG0ApILDm8K9SysAuHdQeghBCAkOllD/b5zXAAVLKZfZ5KXCOlPK7XTfK3Z8k8/gcsAlwYC3O/FcpZcWuG+HuixBCAMR/GAghrgROBs5DfUYqFDsVZR5tnb6AX0oZBJBShoEqoN+uHNQeyrvA7VLK64EQMH0Xj2ePRAiRBaQBWxKKy4D+u2ZEezTvAo9IKW8A5gP/FkKoz8MkSJuEohOBJ1GfkQrFTkd9SCl2OFLK6VJKv336HPBrIYR7Fw5JsZcjpXxLSrnZPn0ZGAUU7cIh7REIIS4FfpJSvrOrx6JQ7I0ooa111gApceHCVv1nYpn5FO1ECOESQvRJKApjve+U0NZBpJSVQB3QI6E4D/We7DBCiMHxY1uLFAE8u25Euz9CiAuB/lLK39tF6jNSodjJKKGtFWz/lo+A4+yiY4Cvla9Gh+kJ3J9wfiQwT0pZvWuGs8fzAnACgBBiOBAF/rdLR7Rn8kL8QAhRAtQCP++64ezeCCEuB/pKKW+xzx9Rn5EKxc5HBSK0gRCiH/AIlsNyIfBbKeWKXTqoPQwhRBrwDywH5TosP5ib4o70iuQIIQ4GzgWuAl4E3pBSzrD92p7E8h3qjeUrOHvXjXT3po15nAa4sHwCi4A/q3lMjhDiV8AXwNaE4lop5WD1GalQ7FyU0KZQKBQKhUKxB6DMowqFQqFQKBR7AEpoUygUCoVCodgDUEKbQqFQKBQKxR6AEtoUCoVCoVAo9gCU0KZQKPZ4WuQC7GwfvbtjLAqFQrGjUEKboksIIUYLIWYKIYJCiM32sTOh/o9CiPkJdXo7+/XY7avtdSIVuwghxKlCiMm7ehzJEBYPA5cklI0TQswWQnwrhHgm8f1o158nhPgyvqZmApcKIR5KUq5QKBS7BSrlh6JbsBcunymlnJykbiwwOVldO/qdCZR25lpF92ALzf2klGN38VC2QQhxCzBSSnm2fe7Gysg/Xko5VwjxAfCRlPIRuz4LWAgcI6VckqS/14G5Usp7dtYzKBQKRXtRmjaFQrFHIoQoAO4A/pBQPATQpZRz7fN/A2MT6u8Hnk0msNn8EfizECKvm4erUCgUXUYJbYqdjm0y/VkIIYUQRwsh3hZCLBJCfCKEKGzH9YcIIb4QQvxPCPGVEOIFIUR+Qv0wIcQbQojv7HazhBCnJ9T3STDp3iOEeEQI8akQIiSEuE4I8VaCOfdqIcS/hRArhRBPCyGMNsZ1hW0KlkKIi4QQ/xVCzLGf9bQWbQuFEK8IIX6w7/OJEGJMQn3iGK6xx7BVCDHDrk8TQjwphPjJNvX9IIT4qxAiPaGPi4QQ84QQX9umwj8LIVx23QR7/qQQYrIQ4iV7nn4SQhyW0Mc/gOOBfe2xzBRC7GvX3Whf87m9nyaEyGnxnL3tsa+yn/HP9muyWQjxTkK7Nl/TVjgbWCOlXJ5QFgUSXyPDLkMIcQRwCHB3ax3awtwGu2+FQqHYvZBSqk1tXd6wTFLPtVI3tmUdMBmQwG32uQOYDzzTot3MxGuBfYAG4HT7XAB/A34EjIS+X6bJ/D8CaxmtkiRjXg8MtM9/A1xhHz+HtezWSfZ5IdZi9+duZx7G2s/1AeC0y84HYsAo+9wLLAceTRjjRCCAZYYkYQy1wBn2+b5YSzEJ4EvgE8Bl1w0EqoF97fPL7fP4s/mAWcATCf33s8f6n4R+HgeWt3im57BM3y2fdSUwwj7W7HbvJ9QL4Ht7LuKvzYVYQlSHXtNW5noG8GGLMgewGhhvH38DXIy1ZNUSYFw73ssfAW/t6v8ptalNbWpruSlNm2JX8xyAlDKCJaCNaasxcBOwWkr5pn2dBP6OJZgda7eZAVxp1yGl/AlLADglSX+fSilX2u0ek1I+kVBXLqV8367bACxux/jiPCylDNvHLwAbgevt80lY613emTDGN7CExCtb9FMlpXzdbjNfSnkucCRwGPBXKWXIrluJZSastK/7A/BSwrPVY8315UKI1Bb3eCXeD/AxUJSosWuDo+25RUppYgnKJ8S1efY4xwAPSimjdrt/Ya33mUh7XtNk9AQqEgvs99EpWGuNfgZ8aD/3/wH/k1J+LoSYkqDdPDxJvxV23wqFQrFb0aqpR6HoICaWhiQZwq5PxvqE4xogYzv32R/IswMU4mjAGiDuh2QCN9nmMGmfDwV+TtLf2jbutb7FeXvGF6c0fiCllP+/vfsJraOK4jj+PWCFirQqMdVSpWlMCa1Yi0ipRVttxT+LQOmiG3UhonXZuFOxaBdBihZ0oSsLoVXJIuC/hYhJpdqKbWxT2oVKJdCFf6DSqqAS4nFx7rSTyZuXiRlNXvl9YMibe2fuzJvb9J3cM3eemZ0hghCI9/A3MGCTJypeAIrBUqPzuyP9zKcFcffXAcysHVgGPFC4TlcBZ1Nd/p6uYh9AvM8LNNdhZnuBdmIU8hqiL24g+mN12q74BeJjhfUqfdrItaTUZ567nwQeztbNrBt4AlhjZpuBXuIargCGzKzL3X/JNTEOXNfkuCIic0JBm9TlV2BRSd1iSgIAd5/Ir1Ie+OWd9uYzGfuJVOKGNEKWzUJt1PZEg7KyuqrnV9WWbARqBucwEwfc/YUK2xX7AKZ5n+n+u4+BXUBfCkw3AcPT7Vtiuj5t5ByRAp3Om8Cz7n7OzB4iUri/AaNmdhZYT6RwMwsojOCJiMwHSo9KXUaBtSV1a4n0ZB1GgJVWeN6bme0xs2wkazMwlAVsyaRndf0PlmcvLIbTOoFTqWiE+N3rzu+QJgRsr9B2NjNyZYP9b3X3n4kRtVWF+qvN7IAVnltWwcVRUjO70swWAhuJP/r2Zylepl7j7P3eUihfXliv0qeN/AC0NanHzB4HJty9PxVdweQgdZypf7y2pbZFROYVBW1SlzeAZWa2y8wu/rsys3XANmCwpuO8TIzo7cwdo4e48TxLf44CG7J7t1J67Paajl/Vk2aWjQI9AiwFXknr7wDfAi9l25hZJzFqdbxC20PAISIFnM0GXQ3s5lKw8SLQY2Z3pXpL7f+Ru9euqh+5lC7sTW2PpvUHc+0XA85h4BjQa2nWrZk9ytQUcJU+bWQY6CqrNLPriWuyI1d8CLjfzBZYfANCF/BVYddu4NMmxxURmRtzPRNCy+WzAFuID7szwGHgCDAAdBe26yU+jJ2YfNBBPB9rDPgzlS1OP88TQcNgbv91xAf2KSKAGQBuztV3Eqm7MeADIqA8kdrZRwQgB9OxxtLrhbn9+9O254H3Utlg7lz2NbkGm9L72gZ8SAQt35BmRua2uxF4O9UNExMA7i45h4PA+sL+i4i032ngs3Td7yxs8xgRXB0FPieeUZbNEr0P+DKd6wlgK9CTXnuq25i2XQF8TczE/ALoSOU70/U7Qkz+2Ntg35uI2anfpz55Jp1vcZZw0z4tudZLiHvpVpXU7weeL5QZsIcI1EaA7YX624C/gLa5/n3SokWLluKib0QQqVHuvq4Odx+b05OZB8xsibv/VCj7DnjL3ftqaL8XuAfY6rP8zyyNFr4PfOLur8323ERE6qb0qIj8l941s3uzlZT2XEqkiGfN3V8l7pcsfWDuDPQBxxWwich8pZE2kZqY2dPAU8Aa4kG2u939o+Z7Xd7MbAfxZe6/E7Myx4Hn3P1wzceZMqL3L9po95jEISIyLyloExEREWkBSo+KiIiItAAFbSIiIiItQEGbiIiISAtQ0CYiIiLSAhS0iYiIiLQABW0iIiIiLeAfYFwa7uoshGEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DATASET = 'OGB-Arxiv'\n",
    "\n",
    "fig, axs = plt.subplots()\n",
    "######################################\n",
    "\n",
    "y_mean = projector_mean[:, -1]\n",
    "y_std  = projector_stds[:, -1]\n",
    "x_axis = np.arange(len(y_mean))\n",
    "\n",
    "axs.plot(x_axis, y_mean, linewidth=3, linestyle='-', label='Projector')\n",
    "axs.fill_between(x_axis, y_mean-y_std, y_mean+y_std, alpha=0.15)\n",
    "\n",
    "######################################\n",
    "\n",
    "y_mean = projector_gc_mean[:, -1]\n",
    "y_std  = projector_gc_stds[:, -1]\n",
    "x_axis = np.arange(len(y_mean))\n",
    "\n",
    "axs.plot(x_axis, y_mean, linewidth=3, linestyle='-', label='Projector (+Adap diff)')\n",
    "axs.fill_between(x_axis, y_mean-y_std, y_mean+y_std, alpha=0.15)\n",
    "\n",
    "######################################\n",
    "\n",
    "y_mean = gcn_mean[:, -1]\n",
    "y_std  = gcn_stds[:, -1]\n",
    "x_axis = np.arange(len(y_mean))\n",
    "\n",
    "axs.plot(x_axis, y_mean, linewidth=2, linestyle='--', label='GCN')\n",
    "axs.fill_between(x_axis, y_mean-y_std, y_mean+y_std, alpha=0.15)\n",
    "\n",
    "######################################\n",
    "\n",
    "y_mean = graphsage_mean[:, -1]\n",
    "y_std  = graphsage_stds[:, -1]\n",
    "x_axis = np.arange(len(y_mean))\n",
    "\n",
    "axs.plot(x_axis, y_mean, linewidth=2, linestyle='--', label='GraphSAGE')\n",
    "axs.fill_between(x_axis, y_mean-y_std, y_mean+y_std, alpha=0.15)\n",
    "\n",
    "\n",
    "\n",
    "######################################\n",
    "plt.title('%s: Comparison on Testing accuracy'%DATASET, fontsize=15)\n",
    "axs.set_ylabel('F1-score', fontsize=15)\n",
    "axs.set_xlabel('Unlearn percentage (%)', fontsize=15)\n",
    "axs.xaxis.get_major_locator().set_params(integer=True)\n",
    "axs.grid(True)\n",
    "fig.tight_layout()\n",
    "axs.legend(fontsize=13)\n",
    "# plt.xlim(0.1, 1)\n",
    "\n",
    "legend = axs.legend(loc='center left', bbox_to_anchor=(1, 0.5), title='Macro-AUC', fontsize=13)\n",
    "plt.setp(legend.get_title(),fontsize=14)\n",
    "\n",
    "plt.savefig('%s_test_perform.pdf'%DATASET, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
